Forthcoming Articles

International Journal of Advanced Mechatronic Systems

International Journal of Advanced Mechatronic Systems (IJAMechS)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Advanced Mechatronic Systems (2 papers in press)

Regular Issues

  • An S3LAM and NFPFLU-RESNET50-based adaptive motion planning of order picking and placing using robotics industry automation   Order a copy of this article
    by Basava Ramanjaneyulu Gudivaka, Rajya Lakshmi Gudivaka, Raj Kumar Gudivaka, Dinesh Kumar Reddy Basan, Sri Harsha Grandhi, M.M. Kamruzzaman 
    Abstract: The automatic order picking and placing (OPP) system using robots has improved the entire supply chain in various industries. None of the prevailing works considered adaptive motion planning for OPP. Therefore, this work proposes the spectral stellar simultaneous localisation and mapping (S3LAM) based path selection for effective OPP. Initially, the workspace images are captured and pre-processed. Then, the objects are detected, and the present text is extracted. Meanwhile, the command data are collected and pre-processed. Next, the keywords are extracted, and the similarities between the texts are analysed. The features are extracted from the object-detected image. Then, the features, similarity score, and keywords are given to the Nebula faster power function linear unit residual-network-50 (NFPFLU-RESNET50) classifier to predict the objects. Next, the bounding box coordinates are extracted, and the grasping pose is detected using the fractal harmonic kinematics grasp pose detection (FHK-GPD) technique.
    Keywords: pick and place; robotics industry automation; object recognition; robotic vision; collision avoidance; you only look once; YOLO; fuzzy interference system; FIS.
    DOI: 10.1504/IJAMECHS.2026.10078935
     
  • An attention-driven state-space ARIMA residual learning model with multi-resolution and seasonal features for AQI forecasting   Order a copy of this article
    by Subhashree Mohapatra, Jai Govind Singh, Rasmi Ranjan Panigrahi, Manohar Mishra 
    Abstract: Accurate air quality index (AQI) forecasting requires models capable of handling nonlinear, non-stationary, and seasonally driven dynamics inherent in urban pollution time series. This study introduces an attention-augmented state-space ARIMA residual learning framework for multi-horizon AQI prediction. Initially, ARIMA is employed in a direct forecasting setup to model dominant linear dependencies. The resulting residuals are treated as structured stochastic components and learned via an attention-based state-space model that integrates higher-order statistical descriptors, wavelet-based multi-resolution energy features, and seasonal-trend components extracted using STL decomposition. The attention mechanism performs adaptive weighting of latent state variables conditioned on evolving atmospheric covariates, enabling context-aware nonlinear corrections. Final forecasts are generated by superimposing ARIMA outputs with learned residual estimates under a strict walk-forward validation protocol. Empirical evaluation on long-term AQI datasets from Delhi and Bangkok demonstrates statistically significant reductions in MAE and RMSE, along with improved R2, highlighting enhanced predictive accuracy, robustness, and cross-domain generalisation.
    Keywords: air quality index; AQI forecasting using ARIMA; state-space attention residual learning; SSAR; wavelet transform-based multi-resolution energy patterns; seasonal-trend decomposition using Loess; non-stationary time series; walk-forward forecasting.
    DOI: 10.1504/IJAMECHS.2026.10079976
     

 

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